• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.07a
• /
• pp.313-314
• /
• 2019

본 논문에서는 특징점 검출 알고리즘을 적용하여 ultrasound image에서 특징점을 검출하는 것과 object dectection을 위한 keypoints가 object에 올바르게 위치하는지를 검증하는 실험을 진행한다. 특징점 검출을 위한 알고리즘으로는 Scale Invariant Feature Transform(SIFT)과 Harris corner detection 을 적용하여 검증한다.

• Proceedings of the IEEK Conference
• /
• 2001.06b
• /
• pp.113-116
• /
• 2001

In this paper we present a new hierarchical layout object extraction algorithm, which is based on rectangles rather than edges. The original layout data is modeled as instances connected by wires. Each polygon shape is divided into a set of rectangles and the instances and wires are extracted and recognized from those rectangles together with their connection and size information. We have applied the algorithm to actual layouts. Experiments on several standard cell library demonstrate the effectiveness of the algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.3
• /
• pp.109-118
• /
• 2019

Road information is one of the most significant geospatial data for applications such as transportation, city planning, map generation, LBS (Location-Based Service), and GIS (Geographic Information System) database updates. Robust technologies to acquire and update accurate road information can contribute significantly to geospatial industries. In this study, we analyze the limitations of ACO (Ant Colony Optimization) road extraction, which is a recently introduced object-based road extraction method using high-resolution satellite images. Object-based ACO road extraction can efficiently extract road areas using both spectral and morphological information. This method, however, is highly dependent on object descriptor information and requires manual designations of descriptors. Moreover, reasonable iteration closing point needs to be specified. In this study, we perform improved ACO road extraction on VHR (Very High Resolution) optical satellite image by proposing an optimization stopping criteria and descriptors that complements the limitations of the existing method. The proposed method revealed 52.51% completeness, 6.12% correctness, and a 51.53% quality improvement over the existing algorithm.

• Journal of KIISE:Software and Applications
• /
• v.31 no.11
• /
• pp.1455-1473
• /
• 2004

This paper suggests a technique, TELOR(Technique of Object Extraction Based on Legacy System Interface for Improvement of Object Reusability) for reuse and reengineering by analyzing the Legacy System interface to distill the meaningful information from them and disassemble them into object units which are to be integrated into the next generation systems. The TELOR method consists of a 4 steps procedure: 1) the interface use case analysis step, 2) the interface object dividing step, 3) the object structure modeling step, and 4) the object model integration step. In step 1, the interface structure and information about the interaction between the user and the Legacy System are obtained. In step 2, the interface information is divided into semantic fields. In step 3, studies and models the structural and collaborative relationship among interface objects. Finally, in step 4, object model integration step, integrates the models and improves the integrated model at a higher level. The objects integration model created through TELOR provides a more efficient understanding of the Legacy System and how to apply it to next generation systems.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.12B
• /
• pp.2349-2358
• /
• 1999

Object-based stereoscopic video coding scheme is presented in this paper. In conventional BMA based stereoscopic video coding for low bit rate transmission, image prediction errors such as block artifacts and mosquito phenomena are occurred. In order to reduce these errors, object based coding scheme is adopted. The proposed scheme consists of preprocessing, object extraction, and object update procedures. The preprocessing procedure extracts non-object regions having low reliability for motion and disparity estimation. This procedure prohibits extracting inaccurate objects. For the better prediction of left channel image, the disparity information is added to the object extraction. And the proposed algorithm can reduce the accumulated error through the object update procedure that detects newly emerging objects, merges objects that have the same object-disparity and object motion, and splits object which has large image prediction error. The experimental results show that the proposed algorithms improve the quality of the prediction without block artifacts and mosquito phenomena.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.11
• /
• pp.411-418
• /
• 2018

With the proliferation of artificial intelligence technology, it is becoming important to obtain, store, and utilize scientific data in research and science sectors. A number of methods for extracting meaningful objects such as graphs and tables from research articles have been proposed to eventually obtain scientific data. Existing extraction methods using heuristic approaches are hardly applicable to electronic documents having heterogeneous manuscript formats because they are designed to work properly for some targeted manuscripts. This paper proposes a prototype of an object extraction system which exploits a recent deep-learning technology so as to overcome the inflexibility of the heuristic approaches. We implemented our trained model, based on the Faster R-CNN algorithm, using the Google TensorFlow Object Detection API and also composed an annotated data set from 100 research articles for training and evaluation. Finally, a performance evaluation shows that the proposed system outperforms a comparator adopting heuristic approaches by 5.2%.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.18 no.2
• /
• pp.16-22
• /
• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.4
• /
• pp.85-94
• /
• 2004

Object tracking in a real time image is one of interesting subjects in computer vision and many Practical application fields during the past couple of years. But sometimes existing systems cannot find all objects by recognizing background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which is not influenced by illumination and to remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up Minimum Bounding Rectangle(MBR) using the internal point of detected object, the system tracks object through this MBR In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.2 s.302
• /
• pp.81-86
• /
• 2005

It is not easy to find and extract a moving object from its background. The extraction method is specific as what it is and how its environment is. So recently the more general method which is less affected by its environmental elements is required. So, In this paper we report on the moving object extraction algorithm using the features of the interlaced-image-capturing method which is adopted in the CCD Camera, an afterimage for exposing time and the fact that an afterimage has same color level. Unlike much of existing algorithms it is use oかy one stationary picture to apply this algorithm.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.453-456
• /
• 2003

Compared with other features of the image, color features are less sensitive to noise and background complication. Besides, this adding to object segmentation has more accuracy of image retrieval. This paper presents object segmentation and HAQ(Histogram Analysis and Quantization) algorithm approach to extract features(the object information and the characteristic colors) of an image. The empirical results shows that this method presents exactly spatial and color information of an image as image retrieval's feature.